Atmospheric moisture absorption parameter processing method and device, equipment and storage medium

Abstract

The invention discloses an atmospheric moisture absorption parameter processing method, device and equipment and a storage medium, and the method comprises the steps: calculating a time difference between a total particulate matter concentration data sequence and a cloud condensation nucleus concentration data sequence, correcting the time sequence deviation between the total particulate matter concentration data sequence and the cloud condensation nucleus concentration data sequence; judging whether the temperature of the cloud condensation nucleus counting instrument in each preset time period is kept stable or not, and then rejecting the cloud condensation nucleus concentration data sequence in the time period with unstable temperature; and according to the total particulate matter concentration data sequence and the cloud condensation nucleus concentration data sequence after multi-charge correction, fitting to obtain a corresponding atmospheric aerosol hygroscopic parameter activation particle size. According to the invention, the time sequence deviation between the total particulate matter concentration data sequence and the cloud condensation nucleus concentration data sequence can be automatically corrected, and the cloud condensation nucleus concentration data sequence in a time period with unstable temperature can be eliminated.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric measurement technology, and in particular to a method, device, equipment and storage medium for processing atmospheric hygroscopicity parameters. Background Art

[0002] Cloud condensation nuclei play a key role in cloud formation and rainfall, and indirectly affect the radiation balance of the earth. Cloud Condensation Nuclei Counter (CCNc) can measure the concentration of cloud condensation nuclei at different supersaturations in the atmosphere. Scanning Mobility Particle Sizer (SMPS = DMA + CPC) can measure the concentration of particles of different size segments in the atmosphere. Moore et al. proposed a measurement combining CCNc and SMPS (SMCA method), and the data obtained by both can quickly and effectively obtain the cloud condensation nucleus size spectrum and particle activation size in the atmosphere.

[0003] The prior art uses a data processing method based on VBS excel to process the data obtained by the two instruments to obtain the cloud condensation nucleus size spectrum and the activated particle size. The specific steps are: (1) reading the required data from the original files of the two instruments and extracting the data into a data file of a specific format; (2) copying the extracted data into the data processing file as required; (3) manually fitting the data at each time point, first judging based on the obtained temperature gradient, and judging by the naked eye whether the temperature gradient is stable; (4) manually aligning the tail ends of the particle concentrations obtained by CCN and SMPS, and judging by the naked eye; (5) performing charge correction; (6) modifying the fitting parameters, fitting according to the formula, and obtaining the activated particle size (D50) at that time.

[0004] However, the existing technology has the following defects: (1) Manual data processing requires a lot of time and energy; (2) Since the lengths of the pipes connected to the two instruments are different during measurement, there is a time deviation in the data measured by the two instruments; when judging whether the time series of particle concentrations obtained by the two instruments overlap, the existing technology requires visual judgment, which is not scientific and accurate; (3) Since the temperature gradient of the cloud condensation nucleus counter (CCNc) changes when the supersaturation is changed, the existing technology needs to rely on personal experience when judging whether the temperature gradient is stable, which is not scientific and accurate. Summary of the invention

[0005] The present invention provides a method, device, equipment and storage medium for processing atmospheric hygroscopicity parameters to solve the technical problem of inaccurate judgment of time series deviation and temperature gradient stability when manually processing particulate matter concentration data and cloud condensation nucleus concentration data.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for processing atmospheric hygroscopicity parameters, comprising:

[0007] Obtaining total particle concentration data generated by a particle counter and cloud condensation nucleus concentration data generated by a cloud condensation nucleus counter;

[0008] Sorting the total particle concentration data in chronological order to obtain a corresponding total particle concentration data sequence, and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0009] Calculating the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correcting the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference;

[0010] Determine whether the temperature of the cloud condensation nucleus counter remains stable in each preset time period, and then remove the cloud condensation nucleus concentration data sequence in the time period with unstable temperature according to the calibrated cloud condensation nucleus concentration data sequence;

[0011] Multi-charge correction is performed on the corrected total particle concentration data series and the cloud condensation nucleus concentration data series after data removal, and then the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is fitted based on the total particle concentration data series and the cloud condensation nucleus concentration data series after multi-charge correction.

[0012] As a preferred solution, before sorting the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence, and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence, the method further includes:

[0013] Obtaining a preset supersaturation of a cloud condensation nucleus counter, and determining a time series range and an activated particle size range according to the supersaturation;

[0014] The total particle concentration data and the cloud condensation nucleus concentration data are screened according to the time series range and the activated particle size range, and the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range are eliminated.

[0015] As a preferred solution, the calculating the time difference between the total particulate matter concentration data sequence and the cloud condensation nucleus concentration data sequence comprises:

[0016] According to a preset measurement cycle, extracting a number of first data sequences of a first time length from the total particulate matter concentration data sequence, and extracting a number of second data sequences of a second time length from the cloud condensation nucleus concentration data sequence;

[0017] In the same measurement period, a first mean square error is calculated between the first data sequence and the second data sequence, and a third data sequence at one time point forward and a fourth data sequence at one time point backward of the cloud condensation nucleus concentration data sequence are respectively extracted, and a second mean square error is calculated between the third data sequence and the fourth data sequence;

[0018] The time difference between the total particle concentration data series and the cloud condensation nucleus concentration data series is calculated according to the first mean square error and the second mean square error.

[0019] As a preferred solution, the first mean square error is calculated by the following formula:

[0020]

[0021] Among them, σ t is the first mean square error corresponding to time point t, N CN,t,i is the first data sequence, N CCN,t,i is the second data sequence, and n is the measurement period.

[0022] As a preferred solution, the method of determining whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period includes:

[0023] Obtain the temperature gradient time series corresponding to the cloud condensation nucleus counter;

[0024] According to the preset sliding window length, the sliding average value corresponding to each time point in the temperature gradient time series is calculated, and then according to the sliding average value, it is judged whether the temperature of the cloud condensation nucleus counter remains stable in each preset time period.

[0025] As a preferred solution, the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is fitted according to the following formula:

[0026]

[0027] Among them, N CCN is the cloud condensation nucleus concentration data series after multi-charge correction, N CN is the total particle concentration data series after multi-charge correction, D P is the particle size value measured by scanning mobility particle size spectrometer, D 50 To fit the parameters to be obtained, B and C are fitting parameters.

[0028] On the basis of the above-mentioned embodiment, another embodiment of the present invention provides a processing device for atmospheric hygroscopicity parameters, comprising: a data acquisition module, a data sorting module, a time correction module, a data elimination module and a data fitting module;

[0029] The data acquisition module is used to acquire total particle concentration data generated by the particle counter and cloud condensation nucleus concentration data generated by the cloud condensation nucleus counter;

[0030] The data sorting module is used to sort the total particle concentration data in chronological order to obtain a corresponding total particle concentration data sequence, and to sort the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0031] The time correction module is used to calculate the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correct the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference;

[0032] The data elimination module is used to determine whether the temperature of the cloud condensation nucleus counter remains stable in each preset time period, and then eliminate the cloud condensation nucleus concentration data sequence in the time period with unstable temperature according to the corrected cloud condensation nucleus concentration data sequence;

[0033] The data fitting module is used to perform multi-charge correction on the corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after data removal, and then fit the corresponding atmospheric aerosol hygroscopicity parameter activation particle size according to the multi-charge corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence.

[0034] As a preferred solution, the atmospheric hygroscopicity parameter processing device further includes: a data preprocessing module;

[0035] The data preprocessing module is used to obtain the supersaturation of a preset cloud condensation nucleus counter, and determine a time series range and an activated particle size range according to the supersaturation; according to the time series range and the activated particle size range, the total particle concentration data and the cloud condensation nucleus concentration data are screened, and the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range are eliminated.

[0036] Based on the above embodiments, another embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the method for processing atmospheric hygroscopicity parameters described in the above invention embodiments is implemented.

[0037] Based on the above embodiments, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for processing atmospheric hygroscopicity parameters described in the above invention embodiment.

[0038] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0039] The present invention provides a method for processing atmospheric hygroscopicity parameters. Through the present invention, the atmospheric hygroscopicity parameters can be automatically processed, and the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence is calculated according to the total particle concentration data generated by the particle counter and the cloud condensation nucleus concentration data generated by the cloud condensation nucleus counter, and the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence is automatically corrected according to the time difference, thereby avoiding the problem that the prior art needs to be manually corrected and needs to be judged based on subjective experience. In addition, the present invention will also automatically determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period, and then according to the corrected cloud condensation nucleus concentration data sequence, the cloud condensation nucleus concentration data sequence within the time period with unstable temperature is eliminated, thereby avoiding the problem that the prior art needs to be judged based on subjective experience and has no objective and scientific standards. Finally, multi-charge correction is performed on the corrected total particle concentration data series and the cloud condensation nucleus concentration data series after data removal, and then the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is fitted based on the total particle concentration data series and the cloud condensation nucleus concentration data series after multi-charge correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a method for processing atmospheric hygroscopicity parameters provided by one embodiment of the present invention;

[0041] Figure 2 It is a data processing software interface diagram of the present invention;

[0042] Figure 3 is a data fitting flow chart of the present invention;

[0043] Figure 4 It is a schematic diagram of the structure of a device for processing atmospheric hygroscopicity parameters provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0046] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0047] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0049] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0050] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0051] Embodiment 1

[0052] Please refer to Figure 1 , is a flow chart of a method for processing atmospheric hygroscopicity parameters provided by an embodiment of the present invention, comprising the following specific steps:

[0053] S1. Obtaining total particle concentration data generated by a particle counter and cloud condensation nucleus concentration data generated by a cloud condensation nucleus counter;

[0054] Preferably, before sorting the total particle concentration data in chronological order to obtain a corresponding total particle concentration data sequence, and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence, it also includes: obtaining a preset supersaturation of a cloud condensation nucleus counter, and determining a time series range and an activated particle size range according to the supersaturation; and screening the total particle concentration data and the cloud condensation nucleus concentration data according to the time series range and the activated particle size range, and eliminating the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range.

[0055] Specifically, the following is a detailed description of the processing method of atmospheric hygroscopic parameters of the present invention, taking the online observation experiment of hygroscopic parameters conducted during the 2022 South China Sea scientific expedition as an example:

[0056] Step 1: The particles enter the differential mobility analyzer (DMA) from the air intake pipe for particle size screening, and then enter the particle counter (CPC) and cloud condensation nucleus counter (CCNc) to calculate the total particle number concentration and cloud condensation nucleus number concentration, and finally generate two data files. In a specific embodiment, according to the time set by the DMA, usually 5-8 minutes is a scanning cycle, so two segments of total particle concentration data and cloud condensation nucleus concentration data will be obtained every 5-8 minutes to calculate the atmospheric aerosol hygroscopicity parameters.

[0057] Step 2: Please refer to Figure 2, is the interface diagram of the data processing software of the present invention. After filling in the folder location of the data files generated by the two instruments and setting the measurement time and other related parameters according to the prompts, run the software and the software will start processing the data files of the two instruments. The specific parameters that need to be set are as follows:

[0058] (1) The path where the cloud condensation nucleus data file is stored and the last two digits of the current measurement year;

[0059] (2) The path where the scanning mobility spectrometer data is located, the number of time lines in the data file, the number of dN / d logDp data start lines, the number of particle size lines, the total time of a scan, and the number of the starting line of the raw data;

[0060] (3) Select the column used for CCNc instrument measurement. If CCNc has only one column, column A is selected by default. Then enter the path of the CCN_uptake_A / B.csv file generated in step 1 and the path where the images of the fitting process are saved.

[0061] (4) Input the parameters required for fitting: The first step is to estimate the time difference between the particles coming out of DMA and entering CCNc and CPC; the second step is to fill in the supersaturation set on the CCNc instrument during the measurement process; the third step is to select the fitting time series range according to different supersaturations; the fourth step is to select the fitting activation particle size range (the upper and lower limits of the activation particle size) according to different supersaturations, and then screen the total particle concentration data and the cloud condensation nucleus concentration data according to the time series range and the activation particle size range, and eliminate the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activation particle size range; the fifth step is to select the range for matching in the tail matching algorithm; the sixth step is to select the temperature gradient judgment algorithm, and method I is selected by default, which is the main judgment algorithm of the present invention;

[0062] (5) Fill in the initial guess value of the activated particle size for different supersaturation fittings, and the range must be within the set upper and lower limits.

[0063] S2. Sort the total particle concentration data in chronological order to obtain a corresponding total particle concentration data sequence, and sort the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0064] Step 3: Please refer to Figure 3 , which is a data fitting flow chart of the present invention. First, the measurement time error between the two instruments caused by factors such as pipeline and air flow velocity differences is automatically corrected by the tail matching algorithm. The specific steps are as follows:

[0065] (1) extracting data from data files of two instruments, sorting the total particle concentration data according to the time sequence in the data files to obtain a corresponding total particle concentration data sequence, and sorting the cloud condensation nucleus concentration data according to the time sequence to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0066] S3, calculating the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correcting the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference;

[0067] Preferably, the calculation of the time difference between the total particle matter concentration data sequence and the cloud condensation nucleus concentration data sequence includes: according to a preset measurement period, extracting a number of first data sequences of a first time length from the total particle matter concentration data sequence, and extracting a number of second data sequences of a second time length from the cloud condensation nucleus concentration data sequence; within the same measurement period, calculating the first mean square error of the first data sequence and the second data sequence, and respectively extracting a third data sequence of the cloud condensation nucleus concentration data sequence at a previous time point and a fourth data sequence at a later time point, and calculating the second mean square error of the third data sequence and the fourth data sequence; and calculating the time difference between the total particle matter concentration data sequence and the cloud condensation nucleus concentration data sequence based on the first mean square error and the second mean square error.

[0068] Preferably, the first mean square error is calculated by the following formula:

[0069]

[0070] Among them, σ t is the first mean square error corresponding to time point t, N CN,t,i is the first data sequence, N CCN,t,i is the second data sequence, and n is the measurement period.

[0071] (2) The time difference between the two instruments is corrected by the tail matching algorithm to ensure that the two time series of the total particulate matter concentration data series and the cloud condensation nucleus concentration data series are measured at the same time.

[0072] The tail matching algorithm is as follows:

[0073] First, according to the start time of a SMPS scan and the total time of a SMPS scan (e.g., 120 seconds, s), a time series N of total particle data is selected. CN,t (i.e., the first data series) and a time series N of cloud condensation nucleus concentration data CCN,t (i.e. the second data sequence), assuming N CN,tThe time length is 120s (i.e. the first time length), N CCN,t The time length is 180 (ie the second time length).

[0074] Since particles will eventually activate into cloud condensation nuclei after growing to a certain size (about >80-180nm) at different supersaturation levels, the same measurement period N CN,t and N CCN,t The number concentrations at the tail of the time series should be equal or very close. Therefore, according to the matching range n selected above, for N CN,t and N CCN,t The mean square error is calculated for the values ​​within this range to obtain the first mean square error Then put N CCN,t The sequence moves forward one time point to get N CCN,t-1 (i.e., the third data sequence), or move back one time point to get N CCN,t+1 (i.e. the fourth data sequence), and then calculate σ t-1 or t+1 (Second mean square error); Repeat the above steps 60 times to obtain 60 σ. Finally, select the σ with the smallest value k The corresponding time point k is the time difference between the two time series scanned at time t. Then, the time series deviation between the total particle concentration data series and the cloud condensation nucleus concentration data series is corrected according to the time difference.

[0075] S4, determining whether the temperature of the cloud condensation nucleus counter remains stable in each preset time period, and then eliminating the cloud condensation nucleus concentration data sequence in the time period with unstable temperature according to the calibrated cloud condensation nucleus concentration data sequence;

[0076] Preferably, the determination of whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period includes: obtaining a temperature gradient time series corresponding to the cloud condensation nucleus counter; calculating a sliding average corresponding to each time point in the temperature gradient time series according to a preset sliding window length, and then determining whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period based on the sliding average.

[0077] Step 4: Use the temperature gradient judgment algorithm to determine whether the temperature in the cloud condensation nucleus counter remains stable during the time measurement data. If it remains stable, enter the final step of calculating the hygroscopicity parameters. If the temperature is unstable, discard the cloud condensation nucleus concentration data for this time period.

[0078] The specific implementation method of the temperature gradient judgment algorithm is as follows:

[0079] The scan started at time t obtains a temperature gradient time series ΔTt Set the sliding window to 5 and calculate the sliding average of each time point n in the time series Then, all the obtained τ are screened, if τ n -τ n-1 The absolute value of is greater than 10(abs(τ n -τ n-1 )>10), the temperature of the data measured at time t is unstable and cannot be fitted in the next step. It should be removed from the cloud condensation nucleus concentration data sequence. Otherwise, it can pass the screening and enter the next step of fitting.

[0080] S5. Perform multi-charge correction on the corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after data removal, and then fit the corresponding atmospheric aerosol hygroscopicity parameter activation particle size based on the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after multi-charge correction.

[0081] Preferably, the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is fitted according to the following formula:

[0082]

[0083] Among them, N CCN is the cloud condensation nucleus concentration data series after multi-charge correction, N CN is the total particle concentration data series after multi-charge correction, D P is the particle size value measured by scanning mobility particle size spectrometer, D 50 To fit the parameters to be obtained, B and C are fitting parameters.

[0084] Step 5: Perform multi-charge correction on the corrected total particle concentration data series and the cloud condensation nucleus concentration data series after data removal. Finally, the atmospheric aerosol hygroscopicity parameter activation particle size D is obtained by fitting. 50 , the specific fitting formula is as follows: N CCN and N CCN is the value of cloud condensation nucleus concentration and total particulate matter obtained by the instrument, D P is the particle size value measured by the scanning mobility particle size spectrometer, D 50 is the parameter to be obtained by fitting, and B and C are the fitting parameters.

[0085] It can be seen that the present invention provides a method for processing atmospheric hygroscopicity parameters. In view of the problem that the prior art requires multiple steps and manual processing to calculate the atmospheric hygroscopicity parameters, which is extremely time-consuming, the present invention integrates all the steps and performs unified automation processing; in view of the problem that the prior art requires manual and subjective calibration of the time error generated when measuring two instruments, the present invention automatically corrects it through the tail matching algorithm; in view of the problem that the prior art requires subjective judgment on whether the temperature gradient is stable, the present invention establishes scientific standards through a temperature gradient judgment algorithm and performs automatic judgment.

[0086] In general, the present invention can achieve the following beneficial effects:

[0087] (1) The entire atmospheric aerosol hygroscopicity parameter calculation process is integrated to quickly obtain accurate atmospheric aerosol hygroscopicity parameters during online observation;

[0088] (2) Through the tail matching algorithm, the measurement time error of the two instruments caused by factors such as pipelines and air flow velocity differences can be automatically and accurately corrected, making the atmospheric hygroscopicity parameters finally obtained more accurate. The prior art requires manual correction and judgment based on subjective experience. The present invention basically overcomes the above shortcomings and makes the processing process more scientific and effective;

[0089] (3) Through the temperature gradient algorithm, the time period when the temperature gradient is unstable can be automatically and accurately screened out; the prior art requires subjective experience to judge, and there is no objective scientific standard. The present invention basically solves this problem, establishes clear judgment standards, and makes the data processing process more scientific and accurate.

[0090] Embodiment 2

[0091] Please refer to Figure 4 , is a schematic diagram of the structure of a device for processing atmospheric hygroscopicity parameters provided by an embodiment of the present invention, the device comprising: a data acquisition module, a data sorting module, a time correction module, a data elimination module and a data fitting module;

[0092] The data acquisition module is used to acquire total particle concentration data generated by the particle counter and cloud condensation nucleus concentration data generated by the cloud condensation nucleus counter;

[0093] The data sorting module is used to sort the total particle concentration data in chronological order to obtain a corresponding total particle concentration data sequence, and to sort the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0094] The time correction module is used to calculate the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correct the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference;

[0095] The data elimination module is used to determine whether the temperature of the cloud condensation nucleus counter remains stable in each preset time period, and then eliminate the cloud condensation nucleus concentration data sequence in the time period with unstable temperature according to the corrected cloud condensation nucleus concentration data sequence;

[0096] The data fitting module is used to perform multi-charge correction on the corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after data removal, and then fit the corresponding atmospheric aerosol hygroscopicity parameter activation particle size according to the multi-charge corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence.

[0097] Preferably, the processing device for the atmospheric hygroscopicity parameters also includes: a data preprocessing module; the data preprocessing module is used to obtain the supersaturation of a preset cloud condensation nucleus counter, and determine a time series range and an activated particle size range according to the supersaturation; according to the time series range and the activated particle size range, the total particle concentration data and the cloud condensation nucleus concentration data are screened, and the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range are eliminated.

[0098] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0099] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0100] Embodiment 3

[0101] Accordingly, an embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for processing atmospheric hygroscopicity parameters described in the above-mentioned embodiment of the invention when executing the computer program.

[0102] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0103] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and various interfaces and lines are used to connect various parts of the entire device.

[0104] Embodiment 4

[0105] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for processing atmospheric hygroscopicity parameters described in the above-mentioned embodiment of the invention.

[0106] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0107] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-On l yMemory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0108] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for processing atmospheric hygroscopicity parameters, characterized in that: include: Obtaining total particle concentration data generated by a particle counter and cloud condensation nucleus concentration data generated by a cloud condensation nucleus counter; Sorting the total particle concentration data in chronological order to obtain a corresponding total particle concentration data sequence, and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence; Calculating the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correcting the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference; Determine whether the temperature of the cloud condensation nucleus counter remains stable in each preset time period, and then remove the cloud condensation nucleus concentration data sequence in the time period with unstable temperature according to the calibrated cloud condensation nucleus concentration data sequence; Multi-charge correction is performed on the corrected total particle concentration data series and the cloud condensation nucleus concentration data series after data removal, and then the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is fitted based on the total particle concentration data series and the cloud condensation nucleus concentration data series after multi-charge correction.

2. The method for processing atmospheric hygroscopicity parameters according to claim 1, characterized in that: Before sorting the total particle concentration data in chronological order to obtain a corresponding total particle concentration data sequence, and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence, the method further includes: Obtaining a preset supersaturation of a cloud condensation nucleus counter, and determining a time series range and an activated particle size range according to the supersaturation; The total particle concentration data and the cloud condensation nucleus concentration data are screened according to the time series range and the activated particle size range, and the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range are eliminated.

3. The method for processing atmospheric hygroscopicity parameters according to claim 1, characterized in that: The calculating the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence comprises: According to a preset measurement cycle, extracting a number of first data sequences of a first time length from the total particulate matter concentration data sequence, and extracting a number of second data sequences of a second time length from the cloud condensation nucleus concentration data sequence; In the same measurement period, a first mean square error is calculated between the first data sequence and the second data sequence, and a third data sequence at one time point forward and a fourth data sequence at one time point backward of the cloud condensation nucleus concentration data sequence are respectively extracted, and a second mean square error is calculated between the third data sequence and the fourth data sequence; The time difference between the total particle concentration data series and the cloud condensation nucleus concentration data series is calculated according to the first mean square error and the second mean square error.

4. The method for processing atmospheric hygroscopicity parameters according to claim 3, characterized in that: The first mean square error is calculated by the following formula: Among them, σ t is the first mean square error corresponding to time point t, N CN,t,i is the first data sequence, N CCN,t,i is the second data sequence, and n is the measurement period.

5. The method for processing atmospheric hygroscopicity parameters according to claim 1, characterized in that: The determining whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period includes: Obtain the temperature gradient time series corresponding to the cloud condensation nucleus counter; According to the preset sliding window length, the sliding average value corresponding to each time point in the temperature gradient time series is calculated, and then according to the sliding average value, it is judged whether the temperature of the cloud condensation nucleus counter remains stable in each preset time period.

6. The method for processing atmospheric hygroscopicity parameters according to claim 1, characterized in that: According to the following formula, the corresponding atmospheric aerosol hygroscopic parameter activation particle size is fitted: Among them, N CCN is the cloud condensation nucleus concentration data series after multi-charge correction, N CN is the total particle concentration data series after multi-charge correction, D P is the particle size value measured by scanning mobility particle size spectrometer, D 50 To fit the parameters to be obtained, B and C are fitting parameters.

7. A device for processing atmospheric hygroscopicity parameters, characterized in that: include: Data acquisition module, data sorting module, time correction module, data rejection module and data fitting module; The data acquisition module is used to acquire total particle concentration data generated by the particle counter and cloud condensation nucleus concentration data generated by the cloud condensation nucleus counter; The data sorting module is used to sort the total particle concentration data in chronological order to obtain a corresponding total particle concentration data sequence, and to sort the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence; The time correction module is used to calculate the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correct the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference; The data elimination module is used to determine whether the temperature of the cloud condensation nucleus counter remains stable in each preset time period, and then eliminate the cloud condensation nucleus concentration data sequence in the time period with unstable temperature according to the corrected cloud condensation nucleus concentration data sequence; The data fitting module is used to perform multi-charge correction on the corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after data removal, and then fit the corresponding atmospheric aerosol hygroscopicity parameter activation particle size according to the multi-charge corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence.

8. The atmospheric hygroscopicity parameter processing device according to claim 7, characterized in that: Also includes: Data preprocessing module; The data preprocessing module is used to obtain the supersaturation of a preset cloud condensation nucleus counter, and determine a time series range and an activated particle size range according to the supersaturation; according to the time series range and the activated particle size range, the total particle concentration data and the cloud condensation nucleus concentration data are screened, and the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range are eliminated.

9. An electronic device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for processing atmospheric hygroscopicity parameters as claimed in any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute the method for processing atmospheric hygroscopicity parameters according to any one of claims 1 to 6.

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